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A Hybrid VAE Based Network Embedding Method for Biomedical Relation Mining

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Abstract

Mining biomedical entity association and extracting the implicit knowledge from biomedical entity relation networks are important for precision medicine. In this paper, we propose a novel method for implicit relation mining from biomedical multi-entity network. In the embedding part, we combine two kinds of model (1) the graph representation learning model like GraphGAN and (2) the network embedding model like VAE based SDNE, to construct a hybrid model GVS. In the prediction part, the positive samples selected from original network and the negative samples generated by ranking meta-paths are used to train kNN. To evaluate the performances of GVS, we compare the proposed method with three state-of-the-art methods (Katz, Catapult and IMC) on benchmark datasets. Moreover, we evaluate GVS on a real biomedical entity relation network, it shows advantages compared with other network embedding methods and successfully mines implicit relationships which validated by PubMed.

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Acknowledgements

This work is supported by the Development Project of Jilin Province of China (Nos. 20200801033GH,YDZJ202101ZYTS128), Jilin Provincial Key Laboratory of Big Data Intelligent Computing (No.20180622002JC), The Fundamental Research Funds for the Central University, JLU.

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, China

    Tian Bai, Ye Wang & Lan Huang

  2. College of Software, Jilin University, Changchun, China

    Ying Li

  3. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China

    Tian Bai, Ying Li, Ye Wang & Lan Huang

Authors
  1. Tian Bai
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  2. Ying Li
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  3. Ye Wang
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  4. Lan Huang
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Correspondence to Lan Huang.

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Bai, T., Li, Y., Wang, Y. et al. A Hybrid VAE Based Network Embedding Method for Biomedical Relation Mining. Neural Process Lett 55, 81–92 (2023). https://doi.org/10.1007/s11063-021-10454-5

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  • DOI: https://doi.org/10.1007/s11063-021-10454-5

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